OpenRaiser/Intern-Atlas huggingface dataset:
PACEMAKER: Avoiding HeART attacks in storage clusters with disk-adaptive redundancy
Venue: arXiv cs.DC 2021
Year: 2021

Data redundancy provides resilience in large-scale storage clusters, but imposes significant cost overhead. Substantial space-savings can be realized by tuning redundancy schemes to observed disk failure rates. However, prior design proposals for such tuning are unusable in real-world clusters, because the IO load of transitions between schemes overwhelms the storage infrastructure (termed transition overload). This paper analyzes traces for millions of disks from production systems at Google, NetApp, and Backblaze to expose and understand transition overload as a roadblock to disk-adaptive redundancy: transition IO under existing approaches can consume 100% cluster IO continuously for several weeks. Building on the insights drawn, we present PACEMAKER, a low-overhead disk-adaptive redundancy orchestrator. PACEMAKER mitigates transition overload by (1) proactively organizing data layouts to make future transitions efficient, and (2) initiating transitions proactively in a manner that avoids urgency while not compromising on space-savings. Evaluation of PACEMAKER with traces from four large (110K-450K disks) production clusters show that the transition IO requirement decreases to never needing more than 5% cluster IO bandwidth (0.2-0.4% on average). PACEMAKER achieves this while providing overall space-savings of 14-20% and never leaving data under-protected. We also describe and experiment with an integration of PACEMAKER into HDFS.
Smoothness of Schatten Norms and Sliding-Window Matrix Streams
Venue: arXiv cs.DS 2021
Year: 2021

Large matrices are often accessed as a row-order stream. We consider the setting where rows are time-sensitive (i.e. they expire), which can be described by the sliding-window row-order model, and provide the first $(1+ε)$-approximation of Schatten $p$-norms in this setting. Our main technical contribution is a proof that Schatten $p$-norms in row-order streams are smooth, and thus fit the smooth-histograms technique of Braverman and Ostrovsky (FOCS 2007) for sliding-window streams.
Return-Oriented Programming on RISC-V
Venue: arXiv cs.CR 2021
Year: 2021

This paper provides the first analysis on the feasibility of Return-Oriented Programming (ROP) on RISC-V, a new instruction set architecture targeting embedded systems. We show the existence of a new class of gadgets, using several Linear Code Sequences And Jumps (LCSAJ), undetected by current Galileo-based ROP gadget searching tools. We argue that this class of gadgets is rich enough on RISC-V to mount complex ROP attacks, bypassing traditional mitigation like DEP, ASLR, stack canaries, G-Free, as well as some compiler-based backward-edge CFI, by jumping over any guard inserted by a compiler to protect indirect jump instructions. We provide examples of such gadgets, as well as a proof-of-concept ROP chain, using C code injection to leverage a privilege escalation attack on two standard Linux operating systems. Additionally, we discuss some of the required mitigations to prevent such attacks and provide a new ROP gadget finder algorithm that handles this new class of gadgets.
Construction D' Lattices for Power-Constrained Communications
Venue: arXiv cs.IT 2021
Year: 2021

Designs and methods for nested lattice codes using Construction D' lattices for coding and convolutional code lattices for shaping are described. Two encoding methods and a decoding algorithm for Construction D' coding lattices that can be used with shaping lattices for power-constrained channels are given. We construct nested lattice codes with good coding properties, a high shaping gain, and low-complexity encoding and decoding. Convolutional code generator polynomials for Construction A lattices with the greatest shaping gain are given, as a result of an extensive search. It is shown that rate 1/3 convolutional codes provide a more favorable performance-complexity trade-off than rate 1/2 convolutional codes. Tail-biting convolutional codes have higher shaping gain than that of zero-tailed convolutional codes. A design for quasi-cyclic low-density parity-check (QC-LDPC) codes to form Construction D' lattices which have efficient encoding and indexing is presented. The resulting QC-LDPC Construction D' lattices are evaluated using four shaping lattices: the $E_8$ lattice, the $BW_{16}$ lattice, the Leech lattice and our best-found convolutional code lattice, showing a shaping gain of approximately 0.65 dB, 0.86 dB, 1.03 dB and 1.25 dB at dimension 2304.
Explaining Credit Risk Scoring through Feature Contribution Alignment with Expert Risk Analysts
Venue: arXiv cs.LG 2021
Year: 2021

Credit assessments activities are essential for financial institutions and allow the global economy to grow. Building robust, solid and accurate models that estimate the probability of a default of a company is mandatory for credit insurance companies, moreover when it comes to bridging the trade finance gap. Automating the risk assessment process will allow credit risk experts to reduce their workload and focus on the critical and complex cases, as well as to improve the loan approval process by reducing the time to process the application. The recent developments in Artificial Intelligence are offering new powerful opportunities. However, most AI techniques are labelled as blackbox models due to their lack of explainability. For both users and regulators, in order to deploy such technologies at scale, being able to understand the model logic is a must to grant accurate and ethical decision making. In this study, we focus on companies credit scoring and we benchmark different machine learning models. The aim is to build a model to predict whether a company will experience financial problems in a given time horizon. We address the black box problem using eXplainable Artificial Techniques in particular, post-hoc explanations using SHapley Additive exPlanations. We bring light by providing an expert-aligned feature relevance score highlighting the disagreement between a credit risk expert and a model feature attribution explanation in order to better quantify the convergence towards a better human-aligned decision making.
A Fast Heuristic for Gateway Location in Wireless Backhaul of 5G Ultra-Dense Networks
Venue: arXiv cs.NI 2021
Year: 2021

In 5G Ultra-Dense Networks, a distributed wireless backhaul is an attractive solution for forwarding traffic to the core. The macro-cell coverage area is divided into many small cells. A few of these cells are designated as gateways and are linked to the core by high-capacity fiber optic links. Each small cell is associated with one gateway and all small cells forward their traffic to their respective gateway through multi-hop mesh networks. We investigate the gateway location problem and show that finding near-optimal gateway locations improves the backhaul network capacity. An exact p-median integer linear program is formulated for comparison with our novel K-GA heuristic that combines a Genetic Algorithm (GA) with K-means clustering to find near-optimal gateway locations. We compare the performance of KGA with six other approaches in terms of average number of hops and backhaul network capacity at different node densities through extensive Monte Carlo simulations. All approaches are tested in various user distribution scenarios, including uniform distribution, bivariate Gaussian distribution, and cluster distribution. In all cases K-GA provides near-optimal results, achieving average number of hops and backhaul network capacity within 2% of optimal while saving an average of 95% of the execution time.
Feedback modalities for a table setting robot assistant for elder care
Venue: arXiv cs.RO 2021
Year: 2021

The interaction of Older adults with robots requires effective feedback to keep them aware of the state of the interaction for optimum interaction quality. This study examines the effect of different feedback modalities in a table setting robot assistant for elder care. Two different feedback modalities (visual and auditory) and their combination were evaluated for three complexity levels. The visual feedback included the use of LEDs and a GUI screen. The auditory feedback included alerts (beeps) and verbal commands. The results revealed that the quality of interaction was influenced mainly by the feedback modality, and complexity had less influence. The verbal feedback was significantly preferable and increased the involvement of the participants during the experiment. The combination of LED lights and verbal commands increased participants' understanding contributing to the quality of interaction.
BGPeek-a-Boo: Active BGP-based Traceback for Amplification DDoS Attacks
Venue: arXiv cs.CR 2021
Year: 2021

Amplification DDoS attacks inherently rely on IP spoofing to steer attack traffic to the victim. At the same time, IP spoofing undermines prosecution, as the originating attack infrastructure remains hidden. Researchers have therefore proposed various mechanisms to trace back amplification attacks (or IP-spoofed attacks in general). However, existing traceback techniques require either the cooperation of external parties or a priori knowledge about the attacker. We propose BGPeek-a-Boo, a BGP-based approach to trace back amplification attacks to their origin network. BGPeek-a-Boo monitors amplification attacks with honeypots and uses BGP poisoning to temporarily shut down ingress traffic from selected Autonomous Systems. By systematically probing the entire AS space, we detect systems forwarding and originating spoofed traffic. We then show how a graph-based model of BGP route propagation can reduce the search space, resulting in a 5x median speed-up and over 20x for 1/4 of all cases. BGPeek-a-Boo achieves a unique traceback result 60% of the time in a simulation-based evaluation supported by real-world experiments.
Developing an Underwater Network of Ocean Observation Systems with Digital Twin Prototypes -- A Field Report from the Baltic Sea
Venue: arXiv cs.SE 2021
Year: 2021

During the research cruise AL547 with RV ALKOR (October 20-31, 2020), a collaborative underwater network of ocean observation systems was deployed in Boknis Eck (SW Baltic Sea, German exclusive economic zone (EEZ)) in the context of the project ARCHES (Autonomous Robotic Networks to Help Modern Societies). This network was realized via a Digital Twin Prototype approach. During that period different scenarios were executed to demonstrate the feasibility of Digital Twins in an extreme environment such as underwater. One of the scenarios showed the collaboration of stage IV Digital Twins with their physical counterparts on the seafloor. This way, we address the research question, whether Digital Twins represent a feasible approach to operate mobile ad hoc networks for ocean and coastal observation.
Automatically Lock Your Neural Networks When You're Away
Venue: arXiv cs.HC 2021
Year: 2021

The smartphone and laptop can be unlocked by face or fingerprint recognition, while neural networks which confront numerous requests every day have little capability to distinguish between untrustworthy and credible users. It makes model risky to be traded as a commodity. Existed research either focuses on the intellectual property rights ownership of the commercialized model, or traces the source of the leak after pirated models appear. Nevertheless, active identifying users legitimacy before predicting output has not been considered yet. In this paper, we propose Model-Lock (M-LOCK) to realize an end-to-end neural network with local dynamic access control, which is similar to the automatic locking function of the smartphone to prevent malicious attackers from obtaining available performance actively when you are away. Three kinds of model training strategy are essential to achieve the tremendous performance divergence between certified and suspect input in one neural network. Extensive experiments based on MNIST, FashionMNIST, CIFAR10, CIFAR100, SVHN and GTSRB datasets demonstrated the feasibility and effectiveness of the proposed scheme.
Stack of discriminative autoencoders for multiclass anomaly detection in endoscopy images
Venue: arXiv cs.CV 2021
Year: 2021

Wireless Capsule Endoscopy (WCE) helps physicians examine the gastrointestinal (GI) tract noninvasively. There are few studies that address pathological assessment of endoscopy images in multiclass classification and most of them are based on binary anomaly detection or aim to detect a specific type of anomaly. Multiclass anomaly detection is challenging, especially when the dataset is poorly sampled or imbalanced. Many available datasets in endoscopy field, such as KID2, suffer from an imbalance issue, which makes it difficult to train a high-performance model. Additionally, increasing the number of classes makes classification more difficult. We proposed a multiclass classification algorithm that is extensible to any number of classes and can handle an imbalance issue. The proposed method uses multiple autoencoders where each one is trained on one class to extract features with the most discrimination from other classes. The loss function of autoencoders is set based on reconstruction, compactness, distance from other classes, and Kullback-Leibler (KL) divergence. The extracted features are clustered and then classified using an ensemble of support vector data descriptors. A total of 1,778 normal, 227 inflammation, 303 vascular, and 44 polyp images from the KID2 dataset are used for evaluation. The entire algorithm ran 5 times and achieved F1-score of 96.3 +- 0.2% and 85.0 +- 0.4% on the test set for binary and multiclass anomaly detection, respectively. The impact of each step of the algorithm was investigated by various ablation studies and the results were compared with published works. The suggested approach is a competitive option for detecting multiclass anomalies in the GI field.
The Public Life of Data: Investigating Reactions to Visualizations on Reddit
Venue: arXiv cs.HC 2021
Year: 2021

This research investigates how people engage with data visualizations when commenting on the social platform Reddit. There has been considerable research on collaborative sensemaking with visualizations and the personal relation of people with data. Yet, little is known about how public audiences without specific expertise and shared incentives openly express their thoughts, feelings, and insights in response to data visualizations. Motivated by the extensive social exchange around visualizations in online communities, this research examines characteristics and motivations of people's reactions to posts featuring visualizations. Following a Grounded Theory approach, we study 475 reactions from the /r/dataisbeautiful community, identify ten distinguishable reaction types, and consider their contribution to the discourse. A follow-up survey with 168 Reddit users clarified their intentions to react. Our results help understand the role of personal perspectives on data and inform future interfaces that integrate audience reactions into visualizations to foster a public discourse about data.
Quantum projective measurements and the CHSH inequality in Isabelle/HOL
Venue: arXiv cs.LO 2021
Year: 2021

We present a formalization in Isabelle/HOL of quantum projective measurements, a class of measurements involving orthogonal projectors that is frequently used in quantum computing. We also formalize the CHSH inequality, a result that holds on arbitrary probability spaces, which can used to disprove the existence of a local hidden-variable theory for quantum mechanics.
UrbanVCA: a vector-based cellular automata framework to simulate the urban land-use change at the land-parcel level
Venue: arXiv cs.CY 2021
Year: 2021

Vector-based cellular automata (CA) based on real land-parcel has become an important trend in current urban development simulation studies. Compared with raster-based and parcel-based CA models, vector CA models are difficult to be widely used because of their complex data structures and technical difficulties. The UrbanVCA, a brand-new vector CA-based urban development simulation framework was proposed in this study, which supports multiple machine-learning models. To measure the simulation accuracy better, this study also first proposes a vector-based landscape index (VecLI) model based on the real land-parcels. Using Shunde, Guangdong as the study area, the UrbanVCA simulates multiple types of urban land-use changes at the land-parcel level have achieved a high accuracy (FoM=0.243) and the landscape index similarity reaches 87.3%. The simulation results in 2030 show that the eco-protection scenario can promote urban agglomeration and reduce ecological aggression and loss of arable land by at least 60%. Besides, we have developed and released UrbanVCA software for urban planners and researchers.
Discriminative Learning for Probabilistic Context-Free Grammars based on Generalized H-Criterion
Venue: arXiv cs.CL 2021
Year: 2021

We present a formal framework for the development of a family of discriminative learning algorithms for Probabilistic Context-Free Grammars (PCFGs) based on a generalization of criterion-H. First of all, we propose the H-criterion as the objective function and the Growth Transformations as the optimization method, which allows us to develop the final expressions for the estimation of the parameters of the PCFGs. And second, we generalize the H-criterion to take into account the set of reference interpretations and the set of competing interpretations, and we propose a new family of objective functions that allow us to develop the expressions of the estimation transformations for PCFGs.
Robotics During a Pandemic: The 2020 NSF CPS Virtual Challenge -- SoilScope, Mars Edition
Venue: arXiv cs.RO 2021
Year: 2021

Remote sample recovery is a rapidly evolving application of Small Unmanned Aircraft Systems (sUAS) for planetary sciences and space exploration. Development of cyber-physical systems (CPS) for autonomous deployment and recovery of sensor probes for sample caching is already in progress with NASA's MARS 2020 mission. To challenge student teams to develop autonomy for sample recovery settings, the 2020 NSF CPS Challenge was positioned around the launch of the MARS 2020 rover and sUAS duo. This paper discusses perception and trajectory planning for sample recovery by sUAS in a simulation environment. Out of a total of five teams that participated, the results of the top two teams have been discussed. The OpenUAV cloud simulation framework deployed on the Cyber-Physical Systems Virtual Organization (CPS-VO) allowed the teams to work remotely over a month during the COVID-19 pandemic to develop and simulate autonomous exploration algorithms. Remote simulation enabled teams across the globe to collaborate in experiments. The two teams approached the task of probe search, probe recovery, and landing on a moving target differently. This paper is a summary of teams' insights and lessons learned, as they chose from a wide range of perception sensors and algorithms.
Using Non-Verbal Expressions as a Tool in Naming Research
Venue: arXiv cs.SE 2021
Year: 2021

Variable and function names are extremely important for program comprehension. It is therefore also important to study how developers select names. But controlled experiments on naming are hindered by the need to describe to experimental subjects what it is they need to name. Words appearing in these descriptions may then find their way into the names, leading to a bias in the results. We suggest that this problem can be alleviated by using emojis or other small graphics in lieu of key words in the descriptions. A replication of previous work on naming, this time including such emojis and graphics, indeed led to a more diverse and less biased choice of words in the names than when using English descriptions.
Autotuning Benchmarking Techniques: A Roofline Model Case Study
Venue: arXiv cs.DC 2021
Year: 2021

Peak performance metrics published by vendors often do not correspond to what can be achieved in practice. It is therefore of great interest to do extensive benchmarking on core applications and library routines. Since DGEMM is one of the most used in compute-intensive numerical codes, it is typically highly vendor optimized and of great interest for empirical benchmarks. In this paper we show how to build a novel tool that autotunes the benchmarking process for the Roofline model. Our novel approach can efficiently and reliably find optimal configurations for any target hardware. Results of our tool on a range of hardware architectures and comparisons to theoretical peak performance are included. Our tool autotunes the benchmarks for the target architecture by deciding the optimal parameters through state space reductions and exhaustive search. Our core idea includes calculating the confidence interval using the variance and mean and comparing it against the current optimum solution. We can then terminate the evaluation process early if the confidence interval's maximum is lower than the current optimum solution. This dynamic approach yields a search time improvement of up to 116.33x for the DGEMM benchmarking process compared to a traditional fixed sample-size methodology. Our tool produces the same benchmarking result with an error of less than 2% for each of the optimization techniques we apply, while providing a great reduction in search time. We compare these results against hand-tuned benchmarking parameters. Results from the memory-intensive TRIAD benchmark, and some ideas for future directions are also included.
Category Aware Explainable Conversational Recommendation
Venue: arXiv cs.AI 2021
Year: 2021

Most conversational recommendation approaches are either not explainable, or they require external user's knowledge for explaining or their explanations cannot be applied in real time due to computational limitations. In this work, we present a real time category based conversational recommendation approach, which can provide concise explanations without prior user knowledge being required. We first perform an explainable user model in the form of preferences over the items' categories, and then use the category preferences to recommend items. The user model is performed by applying a BERT-based neural architecture on the conversation. Then, we translate the user model into item recommendation scores using a Feed Forward Network. User preferences during the conversation in our approach are represented by category vectors which are directly interpretable. The experimental results on the real conversational recommendation dataset ReDial demonstrate comparable performance to the state-of-the-art, while our approach is explainable. We also show the potential power of our framework by involving an oracle setting of category preference prediction.
A fast and scalable bottom-left-fill algorithm to solve nesting problems using a semi-discrete representation
Venue: arXiv cs.CG 2021
Year: 2021

We present a fast algorithm to solve nesting problems based on a semi-discrete representation of both the 2D non-convex pieces and the strip. The pieces and the strip are represented by a set of equidistant vertical line segments. The discretization algorithm uses a sweep-line method and applies minimal extensions to the line segments of a piece to ensure that non-overlapping placement of the segments, representing two pieces, cannot cause overlap of the original pieces. We implemented a bottom-left-fill greedy placement procedure, using an optimised ordering of the segments overlap tests. The C++ implementation of our algorithm uses appropriate data structures that allow fast execution. It executes the bottom-left-fill algorithm for typical ESICUP data sets in a few milliseconds, even when rotation of the pieces is considered, and thus provides a suitable `building block' for integration in metaheuristics. Moreover, we show that the algorithm scales well when the number of pieces increases.